Regression Analysis Using Asymmetric Losses: A Bayesian Approach

Georgios Tsiotas ()
Additional contact information
Georgios Tsiotas: University of Crete

Journal of Quantitative Economics, 2022, vol. 20, issue 2, No 2, 327 pages

Abstract: Abstract Symmetric loss functions are sometimes inappropriate in Economics prediction problems. Asymmetric loss functions can then be applied where errors of the same magnitude but with a different sign can reflect different loss levels. We develop a Bayesian framework that estimates regression models which incorporate asymmetric loss functions such as the linex loss. Given that the likelihood function is not of a known form, estimation is implemented using a Laplace-type estimator within a Markov Chain Monte Carlo framework. We illustrate this method using simulated and real estate data series. The results demonstrate significant findings with regard to the prediction of linear models that use the linex loss function.

Keywords: Linex loss; MCMC; Laplace type estimator; Real estate market (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40953-022-00289-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jqecon:v:20:y:2022:i:2:d:10.1007_s40953-022-00289-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40953

DOI: 10.1007/s40953-022-00289-9

Access Statistics for this article

Journal of Quantitative Economics is currently edited by Dilip Nachane and P.G. Babu

More articles in Journal of Quantitative Economics from Springer, The Indian Econometric Society (TIES) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().